DSpace Collection:http://openarchive.nure.ua/handle/document/68
Tue, 19 Mar 2019 00:24:35 GMT2019-03-19T00:24:35ZСтатистичні розподіли та ланцюжкове подання даних при визначенні релевантності структурних описів візуальних об’єктівhttp://openarchive.nure.ua/handle/document/7847
Title: Статистичні розподіли та ланцюжкове подання даних при визначенні релевантності структурних описів візуальних об’єктів
Authors: Гороховатський, В. О.; Гадецька, С. В.; Пономаренко, Р. П.
Abstract: The subjects of the paper are the models for estimation of the relevance between images in the space of key point descriptors when recognizing visual objects in computer vision systems. The goal is to create an image structural recognition method based on the implementation of chain data models using probability distributions of the sets of descriptors. The tasks include the development of mathematical and software models of efficient data analysis for determining the relevance of structural descriptions, investigation of the properties, application attributes, values of parameters of these models, evaluation of the effectiveness of the specific image processing. The methods are used: a BRISK detector for forming the key point descriptors, data mining, methods of bitwise processing and building bit-data distributions, a method of metric relevance estimation, software modeling. The following results were obtained. The transition from the sets of descriptors to probability distributions of fragments and the comparison of images in the space of distributions provide the necessary recognition performance. Data processing and analysis are performed hundreds of times faster than traditional vote counting. Processing and analysis of bit combinations forms significant properties for a set of description elements with keeping the data structure and their unification. With an increase of the number of bits in the distribution fragment, the distance between images increases and it contributes to an increase of their difference degree. The chain representation and the use of distributions create a new data space, which allows to improve the performance of image recognition systems significantly. Conclusions. The contribution of the paper is the improvement of the structural image recognition method with the introduction of a generalized chain description structure using the distribution values for fragments of the set of key point descriptors, which meaningfully reflect the properties of image objects and provide effective recognition. The practical significance of the paper is the achievement of an increase of image relevance calculation speed, confirmation of the effectiveness of proposed modifications on sample images, obtaining of an application software models for research and implementation of classification methods in computer vision systems.
Keywords: structural image recognition methods, key point, BRISK detector, chain representation, fragment data distribution, general descriptor, descriptive relevance, voting, Manhattan metric, speed of relevancy estimation.Wed, 02 Jan 2019 00:00:00 GMThttp://openarchive.nure.ua/handle/document/78472019-01-02T00:00:00ZДослідження модифікацій методу встановлення релевантності зображень об’єктів за описами у вигляді множини дескрипторів ключових точокhttp://openarchive.nure.ua/handle/document/7521
Title: Дослідження модифікацій методу встановлення релевантності зображень об’єктів за описами у вигляді множини дескрипторів ключових точок
Authors: Гороховатський, В. О.
Abstract: INVESTIGATION OF THE RELEVANCE IMAGE OBJECTS ESTIMATION METHOD MODIFICATIONS WITH DESCRIPTIONS IN THE FORM OF KEYPOINTS FEATURES SET
V. O. Gorokhovatskyi, A.A. Vasylchenko, K.P. Manko, R.P. Ponomarenko
The subject of the paper is the models for estimation of the relevance degree between images in the space of key points descriptors for the implementation of visual images structural recognition methods in computer vision systems. The goal is the experimental modeling of methods modifications implementations effective in terms of performance for estimation of keypoint descriptors similarity based on the bit data analysis approach. The tasks include the development of mathematical and software data processing models for calculation of the structural descriptions similarity, the investigation of the properties and application features of these models, the effectiveness evaluation according to specific images processing results. The methods are to be used: BRISK detector for forming of key point descriptors, data mining, k-means clustering method, methods of bitwise processing and data entry frequency calculation, the theory of bit data hashing, experimental modeling. Following results are obtained. Image classification methods based on the similarity of key point descriptors are improved and applied using the implementation of the bit data analysis approach. The cluster descriptions representation allows not only to reduce the processing time but also to show the sensitivity of method modification to insignificant image feature and its ability to be widely used in computer vision systems. Hashing the description without losing data is significantly (hundreds of times during modeling) accelerates the process of descriptions relevancy degree calculation. The selected hash function can influence the result and help to increase the level of image distinguishing. The construction of the general description in a form of a common descriptor significantly reduces the computing time, because of which the requirement of a prior description processing in order to form a shortened description from the list of valuable descriptors occurs. Conclusions. The contribution of the paper is to improve the structural image recognition method based on the description as a set of key point features using clustering approach, the identification of generalized properties and data hashing to determine the modified relevance measures of the analyzed and etalon descriptions. The practical significance of the paper is the achievement of a significant increase of image relevance calculation speed, confirmation of the effectiveness of proposed modifications on sample images, obtaining of an application software models for research and implementation of classification methods in computer vision systems.
Keywords: structural image recognition methods, BRISK detector, clustering in descriptor space, generalized descriptor, hashing, relevancy of descriptions, voting, Hamming metric, speed of relevancy determination.Mon, 01 Jan 2018 00:00:00 GMThttp://openarchive.nure.ua/handle/document/75212018-01-01T00:00:00ZAnalysis of Application of Cluster Descriptions in Space of Characteristic Image Featureshttp://openarchive.nure.ua/handle/document/7248
Title: Analysis of Application of Cluster Descriptions in Space of Characteristic Image Features
Authors: Gorokhovatskyi, Volodymyr
Abstract: Abstract: Structural image recognition method modifications in space of characteristic features for recognition of computer vision image dataset were investigated. Recognition performance boost is achieved with quantization (clustering) in the space of image characteristic features that form the pattern of the object. Due to the transformation of structural objects descriptions from a set representation to a vector form, the amount of computation might be reduced tens of times. The results of experiments on Leeds Butterfly dataset that confirmed the effectiveness of decision-making systems based on the proposed approach are shown.Mon, 01 Jan 2018 00:00:00 GMThttp://openarchive.nure.ua/handle/document/72482018-01-01T00:00:00ZЗастосування статистичних мір релевантності для векторних структурних описів об’єктів у задачі класифікації зображеньhttp://openarchive.nure.ua/handle/document/7021
Title: Застосування статистичних мір релевантності для векторних структурних описів об’єктів у задачі класифікації зображень
Authors: Гороховатський, В. О.; Гадецька, С. В.
Abstract: Вирішується задача класифікації зображень у просторі ознак дескрипторів особливих точок з поданням опису у кластерному виді і використанням статистичних мір для обчислення релевантності описів. Проведено аналіз особливостей застосування статистичного та метричного класифікаторів при визначенні рівня релевантності структурних описів. Виконано порівняння характеристик мір релевантності на розрахункових прикладах. Запропоновано використання розходження Кульбака-Лейблера як універсальної і ефективної міри для задачі класифікації. Підтверджена результативність запропонованого підходу для прикладних баз зображень.Mon, 01 Jan 2018 00:00:00 GMThttp://openarchive.nure.ua/handle/document/70212018-01-01T00:00:00Z